Mingtian Zhang

According to our database, Mingtian Zhang authored at least 26 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Towards Training One-Step Diffusion Models Without Distillation.

Mingtian Zhang, Jiajun He, Wenlin Chen, Zijing Ou, José Miguel Hernández-Lobato, Bernhard Schölkopf, David Barber

CoRR, February, 2025

Efficient and Privacy-Preserving Ride Matching Over Road Networks Against Malicious ORH Server.

Mingtian Zhang, Anjia Yang, Jian Weng, Min-Rong Chen, Huang Zeng, Yi Liu, Xiaoli Liu, Zhihua Xia

IEEE Trans. Inf. Forensics Secur., 2025

Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching.

Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Training Neural Samplers with Reverse Diffusive KL Divergence.

Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Diffusion Model With Optimal Covariance Matching.

Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber

CoRR, 2024

Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning.

Mingtian Zhang, Shawn Lan, Peter Hayes, David Barber

CoRR, 2024

Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling.

Jake Cunningham, Giorgio Giannone, Mingtian Zhang, Marc Peter Deisenroth

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Constructing Semantics-Aware Adversarial Examples with a Probabilistic Perspective.

Andi Zhang, Mingtian Zhang, Damon Wischik

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Active Preference Learning for Large Language Models.

William Muldrew, Peter Hayes, Mingtian Zhang, David Barber

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Diffusive Gibbs Sampling.

Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Incorporating neuro-inspired adaptability for continual learning in artificial intelligence.

Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, Yi Zhong

Nat. Mac. Intell., December, 2023

Moment Matching Denoising Gibbs Sampling.

Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Spread Flows for Manifold Modelling.

Mingtian Zhang, Yitong Sun, Chen Zhang, Steven McDonagh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Towards Healing the Blindness of Score Matching.

Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol

CoRR, 2022

Integrated Weak Learning.

Peter Hayes, Mingtian Zhang, Raza Habib, Jordan Burgess, Emine Yilmaz, David Barber

CoRR, 2022

Out-of-Distribution Detection with Class Ratio Estimation.

Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yitong Sun, Steven McDonagh

CoRR, 2022

Improving VAE-based Representation Learning.

Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber

CoRR, 2022

Parallel Neural Local Lossless Compression.

Mingtian Zhang, James Townsend, Ning Kang, David Barber

CoRR, 2022

Generalization Gap in Amortized Inference.

Mingtian Zhang, Peter Hayes, David Barber

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Flow Based Models For Manifold Data.

Mingtian Zhang, Yitong Sun, Steven McDonagh, Chen Zhang

CoRR, 2021

On the Out-of-distribution Generalization of Probabilistic Image Modelling.

Mingtian Zhang, Andi Zhang, Steven McDonagh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

AFEC: Active Forgetting of Negative Transfer in Continual Learning.

Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Spread Divergence.

Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Wasserstein Robust Reinforcement Learning.

Mohammed Amin Abdullah, Hang Ren, Haitham Bou-Ammar, Vladimir Milenkovic, Rui Luo, Mingtian Zhang, Jun Wang

CoRR, 2019

Variational f-divergence Minimization.

Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu, David Barber

CoRR, 2019

2018

Spread Divergences.

David Barber, Mingtian Zhang, Raza Habib, Thomas Bird

CoRR, 2018